Quantitative AI in Complex Fluids and Complex Flows: Challenges and Benchmarks

In this topical collection (19 articles)

1. Editorial

   Topical issue on quantitative AI in complex fluids and complex flows: challenges and benchmarks

   L. Biferale, M. Buzzicotti, M. Cencini Article:102

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2. 

   Regular Article - Flowing Matter

   Inferring turbulent environments via machine learning

   Michele Buzzicotti, Fabio Bonaccorso Article:102

3. 

   Regular Article - Flowing Matter

   Toward accelerated data-driven Rayleigh–Bénard convection simulations

   Ayya Alieva, Stephan Hoyer, Michael Brenner… Article:64

4. 

   Regular Article – Flowing Matter

   Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation

   Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos Article:59

5. 

   Regular Article – Soft Matter

   Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges

   Mark Benjamin, Stefan P. Domino, Gianluca Iaccarino Article:55

6. Regular Article - Flowing Matter

   Optimal navigation of a smart active particle: directional and distance sensing

   Mischa Putzke, Holger Stark Article:48

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7. 

   Regular Article - Flowing Matter

   Inference of relative permeability curves in reservoir rocks with ensemble Kalman method

   Xu-Hui Zhou, Haochen Wang, James McClure, Cheng Chen… Article:44

8. 

   Regular Article – Flowing Matter

   Steering undulatory micro-swimmers in a fluid flow through reinforcement learning

   Zakarya El Khiyati, Raphaël Chesneaux, Laëtitia Giraldi… Article:43

9. Regular Article - Flowing Matter

   Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications

   Mihir Durve, Sibilla Orsini, Adriano Tiribocchi… Article:32

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10. Regular Article - Flowing Matter

    Generative adversarial networks to infer velocity components in rotating turbulent flows

    Tianyi Li, Michele Buzzicotti, Luca Biferale… Article:31

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11. 

    Regular Article - Flowing Matter

    Machine learning for optimal flow control in an axial compressor

    M. A. Elhawary, Francesco Romanò… Article:28

12. Regular Article - Flowing Matter

    Deep reinforcement learning for turbulent drag reduction in channel flows

    Luca Guastoni, Jean Rabault, Philipp Schlatter… Article:27

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13. 

    Regular Article - Flowing Matter

    Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark

    Aurore Loisy, Robin A. Heinonen Article:17

14. 

    Regular Article - Flowing Matter

    Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed Neural Networks

    Patricio Clark Di Leoni, Lokahith Agasthya… Article:16

15. 

    Regular Article - Flowing Matter

    Assimilation of statistical data into turbulent flows using physics-informed neural networks

    Sofía Angriman, Pablo Cobelli, Pablo D. Mininni… Article:13

16. 

    Regular Article - Flowing Matter

    Curriculum learning for data-driven modeling of dynamical systems

    Michele Alessandro Bucci, Onofrio Semeraro… Article:12

17. Regular Article - Flowing Matter

    Toward learning Lattice Boltzmann collision operators

    Alessandro Corbetta, Alessandro Gabbana, Vitaliy Gyrya… Article:10

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18. 

    Regular Article - Flowing Matter

    Taming Lagrangian chaos with multi-objective reinforcement learning

    Chiara Calascibetta, Luca Biferale, Francesco Borra… Article:9

19. 

    Regular Article - Flowing Matter

    Optimizing airborne wind energy with reinforcement learning

    N. Orzan, C. Leone, A. Mazzolini, J. Oyero, A. Celani Article:2